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Erschienen in:
1989 | OriginalPaper | Buchkapitel
Joint Force Sensing for Unified Motor Learning
verfasst von : A. Mukerjee
Erschienen in: Sensor Devices and Systems for Robotics
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Motor learning consists of using in-built sensors to learn more about one’s own motion behavior. In this paper, we present an approach for motor learning based on a perturbed parameter scheme. A technique is developed for determining the link inertias based on joint reaction data, obtained through force sensors. Due to inexactness of the model, the parameters thus estimated are likely to differ from their true values. This perturbed parameter set can be thought of as a “learned” model of the executed motion. Running the dynamics procedure with these altered parameters results in a more accurate prediction of the control torques needed for the desired motion.